# Implementation Plan ## Enhanced Data Provider and COB Integration - [ ] 1. Enhance the existing DataProvider class with standardized model inputs - Extend the current implementation in core/data_provider.py - Implement standardized COB+OHLCV data frame for all models - Create unified input format: 300 frames OHLCV (1s, 1m, 1h, 1d) ETH + 300s of 1s BTC - Integrate with existing multi_exchange_cob_provider.py for COB data - _Requirements: 1.1, 1.2, 1.3, 1.6_ - [ ] 1.1. Implement standardized COB+OHLCV data frame for all models - Create BaseDataInput class with standardized format for all models - Implement OHLCV: 300 frames of (1s, 1m, 1h, 1d) ETH + 300s of 1s BTC - Add COB: ±20 buckets of COB amounts in USD for each 1s OHLCV - Include 1s, 5s, 15s, and 60s MA of COB imbalance counting ±5 COB buckets - Ensure all models receive identical input format for consistency - _Requirements: 1.2, 1.3, 8.1_ - [ ] 1.2. Implement extensible model output storage - Create standardized ModelOutput data structure - Support CNN, RL, LSTM, Transformer, and future model types - Include model-specific predictions and cross-model hidden states - Add metadata support for extensible model information - _Requirements: 1.10, 8.2_ - [ ] 1.3. Enhance Williams Market Structure pivot point calculation - Extend existing williams_market_structure.py implementation - Improve recursive pivot point calculation accuracy - Add unit tests to verify pivot point detection - Integrate with COB data for enhanced pivot detection - _Requirements: 1.5, 2.7_ - [-] 1.4. Optimize real-time data streaming with COB integration - Enhance existing WebSocket connections in enhanced_cob_websocket.py - Implement 10Hz COB data streaming alongside OHLCV data - Add data synchronization across different refresh rates - Ensure thread-safe access to multi-rate data streams - _Requirements: 1.6, 8.5_ - [ ] 1.5. Fix WebSocket COB data processing errors - Fix 'NoneType' object has no attribute 'append' errors in COB data processing - Ensure proper initialization of data structures in MultiExchangeCOBProvider - Add validation and defensive checks before accessing data structures - Implement proper error handling for WebSocket data processing - _Requirements: 1.1, 1.6, 8.5_ - [ ] 1.6. Enhance error handling in COB data processing - Add validation for incoming WebSocket data - Implement reconnection logic with exponential backoff - Add detailed logging for debugging COB data issues - Ensure system continues operation with last valid data during failures - _Requirements: 1.6, 8.5_ ## Enhanced CNN Model Implementation - [ ] 2. Enhance the existing CNN model with standardized inputs/outputs - Extend the current implementation in NN/models/enhanced_cnn.py - Accept standardized COB+OHLCV data frame: 300 frames (1s,1m,1h,1d) ETH + 300s 1s BTC - Include COB ±20 buckets and MA (1s,5s,15s,60s) of COB imbalance ±5 buckets - Output BUY/SELL trading action with confidence scores - _Requirements: 2.1, 2.2, 2.8, 1.10_ - [x] 2.1. Implement CNN inference with standardized input format - Accept BaseDataInput with standardized COB+OHLCV format - Process 300 frames of multi-timeframe data with COB buckets - Output BUY/SELL recommendations with confidence scores - Make hidden layer states available for cross-model feeding - Optimize inference performance for real-time processing - _Requirements: 2.2, 2.6, 2.8, 4.3_ - [x] 2.2. Enhance CNN training pipeline with checkpoint management - Integrate with checkpoint manager for training progress persistence - Store top 5-10 best checkpoints based on performance metrics - Automatically load best checkpoint at startup - Implement training triggers based on orchestrator feedback - Store metadata with checkpoints for performance tracking - _Requirements: 2.4, 2.5, 5.2, 5.3, 5.7_ - [ ] 2.3. Implement CNN model evaluation and checkpoint optimization - Create evaluation methods using standardized input/output format - Implement performance metrics for checkpoint ranking - Add validation against historical trading outcomes - Support automatic checkpoint cleanup (keep only top performers) - Track model improvement over time through checkpoint metadata - _Requirements: 2.5, 5.8, 4.4_ ## Enhanced RL Model Implementation - [ ] 3. Enhance the existing RL model with standardized inputs/outputs - Extend the current implementation in NN/models/dqn_agent.py - Accept standardized COB+OHLCV data frame: 300 frames (1s,1m,1h,1d) ETH + 300s 1s BTC - Include COB ±20 buckets and MA (1s,5s,15s,60s) of COB imbalance ±5 buckets - Output BUY/SELL trading action with confidence scores - _Requirements: 3.1, 3.2, 3.7, 1.10_ - [ ] 3.1. Implement RL inference with standardized input format - Accept BaseDataInput with standardized COB+OHLCV format - Process CNN hidden states and predictions as part of state input - Output BUY/SELL recommendations with confidence scores - Include expected rewards and value estimates in output - Optimize inference performance for real-time processing - _Requirements: 3.2, 3.7, 4.3_ - [ ] 3.2. Enhance RL training pipeline with checkpoint management - Integrate with checkpoint manager for training progress persistence - Store top 5-10 best checkpoints based on trading performance metrics - Automatically load best checkpoint at startup - Implement experience replay with profitability-based prioritization - Store metadata with checkpoints for performance tracking - _Requirements: 3.3, 3.5, 5.4, 5.7, 4.4_ - [ ] 3.3. Implement RL model evaluation and checkpoint optimization - Create evaluation methods using standardized input/output format - Implement trading performance metrics for checkpoint ranking - Add validation against historical trading opportunities - Support automatic checkpoint cleanup (keep only top performers) - Track model improvement over time through checkpoint metadata - _Requirements: 3.3, 5.8, 4.4_ ## Enhanced Orchestrator Implementation - [ ] 4. Enhance the existing orchestrator with centralized coordination - Extend the current implementation in core/orchestrator.py - Implement DataSubscriptionManager for multi-rate data streams - Add ModelInferenceCoordinator for cross-model coordination - Create ModelOutputStore for extensible model output management - Add TrainingPipelineManager for continuous learning coordination - _Requirements: 4.1, 4.2, 4.5, 8.1_ - [ ] 4.1. Implement data subscription and management system - Create DataSubscriptionManager class - Subscribe to 10Hz COB data, OHLCV, market ticks, and technical indicators - Implement intelligent caching for "last updated" data serving - Maintain synchronized base dataframe across different refresh rates - Add thread-safe access to multi-rate data streams - _Requirements: 4.1, 1.6, 8.5_ - [ ] 4.2. Implement model inference coordination - Create ModelInferenceCoordinator class - Trigger model inference based on data availability and requirements - Coordinate parallel inference execution for independent models - Handle model dependencies (e.g., RL waiting for CNN hidden states) - Assemble appropriate input data for each model type - _Requirements: 4.2, 3.1, 2.1_ - [ ] 4.3. Implement model output storage and cross-feeding - Create ModelOutputStore class using standardized ModelOutput format - Store CNN predictions, confidence scores, and hidden layer states - Store RL action recommendations and value estimates - Support extensible storage for LSTM, Transformer, and future models - Implement cross-model feeding of hidden states and predictions - Include "last predictions" from all models in base data input - _Requirements: 4.3, 1.10, 8.2_ - [ ] 4.4. Implement training pipeline management - Create TrainingPipelineManager class - Call each model's training pipeline with prediction-result pairs - Manage training data collection and labeling - Coordinate online learning updates based on real-time performance - Track prediction accuracy and trigger retraining when needed - _Requirements: 4.4, 5.2, 5.4, 5.7_ - [ ] 4.5. Implement enhanced decision-making with MoE - Create enhanced DecisionMaker class - Implement Mixture of Experts approach for model integration - Apply confidence-based filtering to avoid uncertain trades - Support configurable thresholds for buy/sell decisions - Consider market conditions and risk parameters in decisions - _Requirements: 4.5, 4.8, 6.7_ - [ ] 4.6. Implement extensible model integration architecture - Create MoEGateway class supporting dynamic model addition - Support CNN, RL, LSTM, Transformer model types without architecture changes - Implement model versioning and rollback capabilities - Handle model failures and fallback mechanisms - Provide model performance monitoring and alerting - _Requirements: 4.6, 8.2, 8.3_ ## Model Inference Data Validation and Storage - [x] 5. Implement comprehensive inference data validation system - Create InferenceDataValidator class for input validation - Validate complete OHLCV dataframes for all required timeframes - Check input data dimensions against model requirements - Log missing components and prevent prediction on incomplete data - _Requirements: 9.1, 9.2, 9.3, 9.4_ - [ ] 5.1. Implement input data validation for all models - Create validation methods for CNN, RL, and future model inputs - Validate OHLCV data completeness (300 frames for 1s, 1m, 1h, 1d) - Validate COB data structure (±20 buckets, MA calculations) - Raise specific validation errors with expected vs actual dimensions - Ensure validation occurs before any model inference - _Requirements: 9.1, 9.4_ - [ ] 5.2. Implement persistent inference history storage - Create InferenceHistoryStore class for persistent storage - Store complete input data packages with each prediction - Include timestamp, symbol, input features, prediction outputs, confidence scores - Store model internal states for cross-model feeding - Implement compressed storage to minimize footprint - _Requirements: 9.5, 9.6_ - [ ] 5.3. Implement inference history query and retrieval system - Create efficient query mechanisms by symbol, timeframe, and date range - Implement data retrieval for training pipeline consumption - Add data completeness metrics and validation results in storage - Handle storage failures gracefully without breaking prediction flow - _Requirements: 9.7, 11.6_ ## Inference-Training Feedback Loop Implementation - [ ] 6. Implement prediction outcome evaluation system - Create PredictionOutcomeEvaluator class - Evaluate prediction accuracy against actual price movements - Create training examples using stored inference data and actual outcomes - Feed prediction-result pairs back to respective models - _Requirements: 10.1, 10.2, 10.3_ - [ ] 6.1. Implement adaptive learning signal generation - Create positive reinforcement signals for accurate predictions - Generate corrective training signals for inaccurate predictions - Retrieve last inference data for each model for outcome comparison - Implement model-specific learning signal formats - _Requirements: 10.4, 10.5, 10.6_ - [ ] 6.2. Implement continuous improvement tracking - Track and report accuracy improvements/degradations over time - Monitor model learning progress through feedback loop - Create performance metrics for inference-training effectiveness - Generate alerts for learning regression or stagnation - _Requirements: 10.7_ ## Inference History Management and Monitoring - [ ] 7. Implement comprehensive inference logging and monitoring - Create InferenceMonitor class for logging and alerting - Log inference data storage operations with completeness metrics - Log training outcomes and model performance changes - Alert administrators on data flow issues with specific error details - _Requirements: 11.1, 11.2, 11.3_ - [ ] 7.1. Implement configurable retention policies - Create RetentionPolicyManager class - Archive or remove oldest entries when limits are reached - Prioritize keeping most recent and valuable training examples - Implement storage space monitoring and alerts - _Requirements: 11.4, 11.7_ - [ ] 7.2. Implement efficient historical data management - Compress inference data to minimize storage footprint - Maintain accessibility for training and analysis - Implement efficient query mechanisms for historical analysis - Add data archival and restoration capabilities - _Requirements: 11.5, 11.6_ ## Trading Executor Implementation - [ ] 5. Design and implement the trading executor - Create a TradingExecutor class that accepts trading actions from the orchestrator - Implement order execution through brokerage APIs - Add order lifecycle management - _Requirements: 7.1, 7.2, 8.6_ - [ ] 5.1. Implement brokerage API integrations - Create a BrokerageAPI interface - Implement concrete classes for MEXC and Binance - Add error handling and retry mechanisms - _Requirements: 7.1, 7.2, 8.6_ - [ ] 5.2. Implement order management - Create an OrderManager class - Implement methods for creating, updating, and canceling orders - Add order tracking and status updates - _Requirements: 7.1, 7.2, 8.6_ - [ ] 5.3. Implement error handling - Add comprehensive error handling for API failures - Implement circuit breakers for extreme market conditions - Add logging and notification mechanisms - _Requirements: 7.1, 7.2, 8.6_ ## Risk Manager Implementation - [ ] 6. Design and implement the risk manager - Create a RiskManager class - Implement risk parameter management - Add risk metric calculation - _Requirements: 7.1, 7.3, 7.4_ - [ ] 6.1. Implement stop-loss functionality - Create a StopLossManager class - Implement methods for creating and managing stop-loss orders - Add mechanisms to automatically close positions when stop-loss is triggered - _Requirements: 7.1, 7.2_ - [ ] 6.2. Implement position sizing - Create a PositionSizer class - Implement methods for calculating position sizes based on risk parameters - Add validation to ensure position sizes are within limits - _Requirements: 7.3, 7.7_ - [ ] 6.3. Implement risk metrics - Add methods to calculate risk metrics (drawdown, VaR, etc.) - Implement real-time risk monitoring - Add alerts for high-risk situations - _Requirements: 7.4, 7.5, 7.6, 7.8_ ## Dashboard Implementation - [ ] 7. Design and implement the dashboard UI - Create a Dashboard class - Implement the web-based UI using Flask/Dash - Add real-time updates using WebSockets - _Requirements: 6.1, 6.8_ - [ ] 7.1. Implement chart management - Create a ChartManager class - Implement methods for creating and updating charts - Add interactive features (zoom, pan, etc.) - _Requirements: 6.1, 6.2_ - [ ] 7.2. Implement control panel - Create a ControlPanel class - Implement start/stop toggles for system processes - Add sliders for adjusting buy/sell thresholds - _Requirements: 6.6, 6.7_ - [ ] 7.3. Implement system status display - Add methods to display training progress - Implement model performance metrics visualization - Add real-time system status updates - _Requirements: 6.5, 5.6_ - [ ] 7.4. Implement server-side processing - Ensure all processes run on the server without requiring the dashboard to be open - Implement background tasks for model training and inference - Add mechanisms to persist system state - _Requirements: 6.8, 5.5_ ## Integration and Testing - [ ] 8. Integrate all components - Connect the data provider to the CNN and RL models - Connect the CNN and RL models to the orchestrator - Connect the orchestrator to the trading executor - _Requirements: 8.1, 8.2, 8.3_ - [ ] 8.1. Implement comprehensive unit tests - Create unit tests for each component - Implement test fixtures and mocks - Add test coverage reporting - _Requirements: 8.1, 8.2, 8.3_ - [ ] 8.2. Implement integration tests - Create tests for component interactions - Implement end-to-end tests - Add performance benchmarks - _Requirements: 8.1, 8.2, 8.3_ - [ ] 8.3. Implement backtesting framework - Create a backtesting environment - Implement methods to replay historical data - Add performance metrics calculation - _Requirements: 5.8, 8.1_ - [ ] 8.4. Optimize performance - Profile the system to identify bottlenecks - Implement optimizations for critical paths - Add caching and parallelization where appropriate - _Requirements: 8.1, 8.2, 8.3_